Wearable Medical Sensors worked example

Adhesive Patch Yield at 99% target adhesive patch yield: a worked example

What does the result look like when target adhesive patch yield reaches 99%? The full calculation is worked below with real intermediate numbers. Use it when adhesive patch yield in wearable medical sensors needs a clean rate and gap-to-target you can put on a tier board.

The inputs for this scenario

  • Conforming adhesive patches: 8 count (unchanged)
  • Adhesive patches inspected: 250 count (unchanged)
  • Target adhesive patch yield: 99 % (raised for this scenario; the documented default is 95)

Working through the calculation

  • Applying the documented formula (Adhesive patch yield rate = adhesive patch yield count ÷ total adhesive patch yield population × 100) to the inputs above produces each figure below.
  • At this operating point the engine returns 3.2 % for adhesive patch yield rate, the number this scenario is built around.
  • At this operating point the engine returns 95.8 points for adhesive patch yield gap to target.
  • At this operating point the engine returns 8 count for adhesive patch yield count.
  • At this operating point the engine returns 250 count for total adhesive patch yield population.

How this compares with the baseline

  • Against the tool's baseline example, where target adhesive patch yield sits at 95% and the headline result is 3.2 %, this scenario lands almost exactly on the baseline at 3.2 %.
  • A figure at this level is achievable when target adhesive patch yield is genuinely sustained, not just peaked for a shift. A single lot's percentage is noisy at small sample sizes; eight good out of a 250 sample is not a stable process estimate.

Results at a glance

  • Adhesive patch yield rate: 3.2 % (headline result)
  • Adhesive patch yield gap to target: 95.8 points
  • Adhesive patch yield count: 8 count
  • Total adhesive patch yield population: 250 count

Run it with your numbers

  • Every input above is editable in the live Adhesive Patch Yield calculator, which recalculates instantly and can be shared with the inputs intact.

Last reviewed 2026-05-12.